The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Introduction to machine learning: k-nearest neighbors
Open accessAbstract
Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm, and then focuses on how to perform kNN modeling with R. The dataset should be prepared before running the knn() function in R. After prediction of outcome with kNN algorithm, the diagnostic performance of the model should be checked. Average accuracy is the mostly widely used statistic to reflect the kNN algorithm. Factors such as k value, distance calculation and choice of appropriate predictors all have significant impact on the model performance.
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Cites
Distance Metric Learning for Large Margin Nearest Neighbor Classification
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A weighted nearest neighbor algorithm for learning with symbolic features
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The optimal distance measure for nearest neighbor classification
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Too much covariates in a multivariable model may cause the problem of overfitting.
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Self-citationEstimating equations for kappa statistics
Thompson, J. R. (2001) · Statistics in Medicine · doi:10.1002/sim.603 · Cited by 29
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Textual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification
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Metadata
- Authors
- Zhang, Z.
- Venue
- Annals of Translational Medicine
- Publisher
- AME Publishing Company
- Published
- 2016-06
- Volume
- 4
- Issue
- 11
- Pages
- 218-218
- ISSN
- 2305-5839 (ISSN-L)
- 2305-5847
- OpenAlex
- W2342603028
- Cited by
- 1,294
- References
- 8
- FWCI
- 22.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
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